By Ying Lu, Jiqian Fang, Lu Tian, Hua Jin
This ebook offers new and robust complicated statistical equipment which have been utilized in smooth drugs, drug improvement, and epidemiology. a few of these tools have been in the beginning built for tackling scientific difficulties. All 29 chapters are self-contained. each one bankruptcy represents the hot improvement and destiny examine themes for a clinical or statistical department. For the advantage of readers with varied statistical history, every one bankruptcy follows the same type: the reason of scientific demanding situations, statistical principles and methods, statistical equipment and methods, mathematical feedback and history and reference. All chapters are written through specialists of the respective themes.
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Extra info for Advanced Medical Statistics
2,7,13 Begg and Greenes2 developed a bias-correction procedure for estimating sensitivity and specificity under the conditional independence assumption, which requires that selection for verification does not depend on the true disease status directly. Zhou7 extended their method to allow a general model for verification process and derived the maximum likelihood estimators for sensitivity and specificity of a diagnostic test and their corresponding variances. Even though the estimated sensitivity and specificity may be biased using only verified cases, Zhou13 showed that under the conditional independence assumption, the naive estimators of predictive values, based on only verified cases, are unbiased.
11. Philbrick, J. , Horwitz, R. I. and Feinstein, A. R. (1980). Methodologic problems of excerise testing for coronary artery disease: Groups, analysis and bias. American Journal of Cardiology 46: 807–812. 12. Feinstein, A. R. (1975). On the sensitivity, specificity and discrimination of diagnostic tests. Clinical Pharmacology and Therapeutics 17: 104–116. 13. Zhou, X. H. (1994). Effect of verification bias on positive and negative predictive values. Statistics in Medicine 13: 1737–1745. 14. , Connor, R.
D=1 z111i ... z1K2 1i D=0 z110i ... z1K2 0i V =0 u11 ... Total n11 ... V =1 T 1 = K1 ... T2 = 1 ... T 2 = K2 ... zK1 1i ... zK1 K2 1i ... zK1 10i ... zK1 K2 0i u1K2 ... uK1 1 ... uK1 K2 n1K2 ... nK1 1 ... -H. Zhou probability of verifying a patient is modeled by a logistic regression model: log P (Vj = 1 | T1j , T2j , Xj = xi ) = ω (T1j , T2j , xi ) . P (Vj = 0 | T1j , T2j , Xj = xi ) The unknown parameters ω may be estimated by the method of generalized estimating equation (GEE)40 . Denote ω ˆ to be the resulting estimates of ω, and denote the probability of verifying the jth patient by νj = P (Vj = 1 | T1j , T2j , Xj = xi ) .